arXiv:2605.25127v1 Announce Type: cross Abstract: Point clouds are a fundamental 3D representation in computer vision, enabling a wide range of perception tasks. However, real-world point clouds often suffer from degradations such as incompleteness, noise, outliers, and irregular density, caused by sensor limitations or occlusions. Recovering clean and detailed shapes from such degraded data is crucial for downstream applications. While existing learning-based methods achieve progress on individual tasks like completion or denoising, they typically rely on global bottleneck features, which los
The paper addresses a persistent challenge in 3D computer vision where real-world point cloud data often suffers from degradations, at a time when 3D data processing is becoming increasingly critical for various AI applications.
Improving the robustness of point cloud restoration is crucial for the reliability and performance of AI systems operating in physical environments, impacting fields from robotics to autonomous vehicles.
The proposed 'Pseudo-Query Dual Transformer' aims to overcome limitations of current methods by integrating multi-scale local and global information more effectively, potentially leading to more accurate and robust 3D perception.
- · Autonomous vehicle developers
- · Robotics companies
- · 3D reconstruction software providers
- · Computer Vision researchers
Improved reliability of systems relying on 3D point cloud data for environmental understanding.
Faster development and deployment of autonomous systems due to more robust perception modules.
Reduced costs in 3D data acquisition and processing as systems become more tolerant to degraded sensor inputs.
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Read at arXiv cs.LG